mHealth 4 : Monitoring, Modelling, Modifying and Maintaining health-related behavior in a digital world Donna Spruijt-Metz, MFA PhD Director, USC mHealth Collaboratory Research Professor, Psychology & Preventive Medicine University of Southern California Presented at the Mobile and Electronic Health ARC’s 2nd Annual Symposium, Boston University, Nov 2017
Before I start: Thanks to FUNDERS M2FED TEAM NIMHD P60 002564 • Jack Stankovic, John Lach • NSF (IIS-1217464, 1521740) • Kayla De La Haye, Brooke NCCAM 1RO1AT008330 • Bell mHealth Collaboratory, Imagine Health TEAM Institute for Creative Marc Weigensburg, Bas Technology teams: • Weerman, Cheng Kun Wen, • Bill Swartout, Skip Rizzo, Arno Stefan Schneider Harthold, Shinyi Wu, Marientina Gotsis KNOWME TEAM Multiscale, Computational Murali Annavaram, Giselle • Modeling TEAMS Ragusa, Gillian O'Reilly, Adar Misha Pavel, Steven Intille, Emken, Shri Narayanan, Urbashi • Wendy Nilsen, Benjamin Mitra, Gautham Thatte, Ming Li, Marlin, Daniel Rivera, Eric Sangwon Lee, Cheng Kun Wen, Hekler, Pedja Klasnja, Javier Diaz, Luz Castillo
IoT mHealth 4 : • Monitoring, modeling, modifying and maintaining health-related behaviors • in Real-Time • and in Context
This Generation Sensing IoT • The internet of things: – On-body, – Chemical, – Implantable, – Deployable, – Persistent user interface, – Connected
Mobile Technologies: Data-Hungry and Ubiquitous Ambient light Proximity Cameras Accelerometry Gyroscopes GPS Microphones Compass. Apps Phone, email, text Integration Internet, Social networks w/wearable+ deployable sensors Real-time data transfer
mHealth 3 : Monitor, Model & Modify Behavior MONITORING M2FED: Monitoring and Modeling Family Eating Behaviors
Premise: Measuring dietary intake is the ‘wicked problem’ of obesity research • Ask people • Observe people • Sense people • Biological measures • Grab ‘small’ data
People as Complex Systems Embedded within Complex Systems Sensed Continuously in Context
M2FED CyberPhysical System • Smart watches • Smart phones • Microphones • Beacons • EMA • Cloud (Internet) • ML (including deep learning) Stankovic et al, Ubicomp 2016
Ubiquitous measures • Eating (Smartwatch) • Who is in the room (Smartwatch ID & Beacons) • Opening of cabinets, drawers, refrigerator (Beacons) • Speaker Identification (Trained algorithms from sound) • Mood (prosody) • Length of meal (Smartwatch) • Speed of eating (Smartwatch)
Signal-Driven & Scheduled Ecological Momentary Assessment Trigger: Sensed mood Trigger: Sensed eating event Cause of stress, anger, Eating in the absence of happiness, sadness hunger Self-regulation Mindfulness Trigger: Participant – Rule-based schedule reported event or mood Vigor, Fatigue, Text, picture, or sound Anxiety, positive affect recording
What we want to know about eating Concurrent Activities (TV, phone use) Where Mood Stress and When anxiety Kitchen cabinet & With whom refrigerator access Length of event Speed Prior and Post Hunger Level Activities
mHealth 4 : Monitor, Model, Modify & Maintain Behavior MODELING
M2FED: Monitoring and Modeling Family Eating Dynamics + A1 angry tone + Duration C1: EE-4 + + + A1 speed of eating + C1: EE-4 C1 speed of Eating A1 mimic C1 + A1 Stress C1 Stress A1: EE-4 + + + - + + + C1 Self Regulation C2: EE-3 Social Facilitation - + - C2 mimic C1 A1 self-regulation Spruijt-Metz, Lach, Stankovic & de la Haye
Our Current Theories are Static Related- ness Perceived Competence Behavior One Way Ticket Motivation Autonomy/ change Control Self- regulation Support
Dynamic, Multiscale Model Requirements • Dynamic, temporally dense, multiscale relationships • In context • Identify multidimensional generalization spaces • Individual (or idiographic) models • Learning and adaptive • Modular & robust • Conceptually seeded, yet data driven Spruijt-Metz & Nilsen, 2014, Marlin, Hekler, Rivera, Pavel, Jimison, Klasnja, Buman, Spruijt-Metz, (HOTP)
Multidimensional generalization spaces • When? • Where? • For whom? • In which state? • Which dose? • Which particular intervention?
Multidimensional generalization spaces: state-space representations of behavior • An individual’s state represented in a multidimensional state space defined by variables that either: • predict future states or future behaviors (or both) • or the probability that a particular intervention will Intervention response surface for be effective intervention ‘A’ • (or both ) for two state variables Hekler, Michie, Pavel, Rivera, Collins, Jimison, Garnett, Parral, Spruijt-Metz, AJPM 2016
Idiographic vs. Nomothetic Differences between Patterns within one individuals individual
Learning and adaptive Adapting Feedback Sensing change Ongoing measurement
Modular & Robust
Conceptually seeded, yet data driven • What new questions can transdisciplinary teams ask this data? • Where are the useful signals in the current noise? • A new search for meaningful mechanisms • Personalizes adaptively as time-sensitive new data comes in. Spruijt-Metz, Hekler, Saranummi, Intille, Korhonen, Nilsen, Pavel et al TMB 2015
Generativity Barrientos, Rivera, & Collins (2010)
mHealth 3 : Monitor, Model & Modify health-related behavior Modifying Just-In-Time, Adaptive Interventions (JITAIs) (Nahum-Shani et al, Health Psych 2015) Intensively Adaptive Interventions (IAIs) (Riley et al, Current Op Psych 2015)
JITAIs: Just In Time Adaptive Interventions • A JITAI is an adaptive intervention that is: – Delivered via mobile devices – Anytime – Anywhere – When the person is in need and/or vulnerable – When the person is receptive – (Meaningful Moments) (Nahum-Shani, Hekler & Spruijt-Metz, Health Psychology 2015; Heron & Smyth, 2010; Kaplan & Stone, 2013; Riley et al., 2011)
Learning algorithms: Meaningful moments • Receptivity 1 • Availability 2 • Opportune moments 3 • Threshold Conditions 4 – In need and/or vulnerable – Receptive and/or available – Motivated and/or able – What, when, where & for whom? 1 Nahum-Shani, Hekler, Spruijt-Metz, Health Psych 2015 2 Sharmin, Ali, Rahman, Bari, Hossain, Kumar, UbiComp ’ 14 3 Poppinga, Heuten, Boll, Pervasive Computing 2014 4 Hekler, Michie, Spruijt-Metz et al under review
KNOWME Networks • A suite of mobile, Bluetooth-enabled, wireless, wearable sensors • That interface with a mobile phone and secure server • To process data in real time, • Designed specifically for use in overweight minority youth Emken et al, Journal of physical activity & health, 2012; Li et al, IEEE trans. on neural syst. and rehab. engineering, 2010; Thatte et al, IEEE transactions on signal processing, 2011
Your Activity Meter Active Time in the Last 60 Minutes Each bar = 30 seconds 20 bars = 10 minutes Sedentary Time (since the last reset) Total Active Battery Indicator Time for Each Device Total Elapsed Time Sedentary = lying down, sitting, sitting & fidgeting, standing, standing & fidgeting Active = standing playing Wii, slow walking, brisk walking, running
Did SMS Prompts Directly Impact Subsequent Activity? 6000 5500 Accelerometer • 5000 counts were 1,066 4500 Counts counts higher 4000 3500 in the following 10 • 3000 minute period 2500 2000 compared to when • 1500 SMS prompts were 1000 not sent (p<0.0001) 500 0 No prompt vs. Prompt No Prompt Prompt
If behavior change is hard: Maintaining that change is harder • Behavior change methods tend to inhibit, rather than erase, the original behavior. • Behavior change – specific to the “context” in which it is learned. – many ways to relapse – inherently unstable and unsteady process Bouton, Prev Med 2014
King & Queen
Systems that are Aware of the User: SimSensei • Shares with SimCoach the goal of providing information and support to returning veterans • BUT much richer perception of user – Computer vision – Voice tracking – Speech recognition • MultiSense: integrates diverse inputs & infers user state • Simsensi: responds appropriately • Moving to mobile Rizzo, Morency, Bolas, Forbell, Gratch, Hartholt, Marsella, Traum, Lucas et al, Comp in Human Beh, 2014 .
Open for Submissions Part of the Nature Partner Journal series, npj Digital Medicine is published in partnership between Springer Nature and the Scripps Translational Science Institute. npj Digital Medicine will publish the highest quality digital medicine research, including the clinical implementation of digital and mobile technologies, virtual healthcare, data analytic methodologies and innovative sensor development to provide the necessary data and longitudinal monitoring to best inform the broadest medical community. Find out more about the benefits of submitting your paper: nature.com/npjdigitalmed
Thank you! Any questions? Please stay connected! Donna Spruijt-Metz, dmetz@usc.edu
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